main webpage W Topic Machine Learning Frontiers in artificial intelligence and applications PFL-IDGAN:Personalized Federated Learning Framework Based on Interactive Dual Generative Adversarial Networks 2025 Federated learning (FL) enables collaborative model training without direct data exchange, promoting privacy-preserving data utilization. To address performance degradation caused by non-independent and identically distributed (non-IID) data, Personalized Fed… Article Open Machine Learning Open Article Page

Machine Learning

Study of algorithms that improve automatically through experience Machine learning ( ML ) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page

Exploring foci of: Frontiers in artificial intelligence and applications PFL-IDGAN:Personalized Federated Learning Framework Based on Interactive Dual Generative Adversarial Networks 2025 Federated learning (FL) enables collaborative model training without direct data exchange, promoting privacy-preserving data utilization. To address performance degradation caused by non-independent and identically distributed (non-IID) data, Personalized Federated Learning (PFL) allows each client to learn a model tailored to its local distribution. However, real-world personalized scenarios often involve not only data heterogeneity but also model heterogeneity across clients. Existing PFL methods struggle under … Open Article Page

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Explore Machine Learning W Topic Machine Learning Frontiers in artificial intelligence and applications PFL-IDGAN:Personalized Federated Learning Framework Based on Interactive Dual Generative Adversarial Networks 2025 Federated learning (FL) enables collaborative model training without direct data exchange, promoting privacy-preserving data utilization. To address performance degradation caused by non-independent and identically distributed (non-IID) data, Personalized Fed… Article Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page